Atmospheric Aerosol Distribution in 2016–2017 Over the Eastern European Region Based on the GEOS-Chem Model

atmosphere

Article Atmospheric Aerosol Distribution in 2016–2017 over the Eastern European Region Based on the GEOS-Chem Model

Gennadi Milinevsky 1,2,3,* , Natallia Miatselskaya 4, Asen Grytsai 2, Vassyl Danylevsky 5, Andrey Bril 4, Anatoli Chaikovsky 4, Yulia Yukhymchuk 3, Yuke Wang 1, Anatoliy Liptuga 6, Volodymyr Kyslyi 6, Olena Turos 7 and Yuriy Serozhkin 6

1 International Center of Future Science, College of Physics, Jilin University, Changchun 130012, China; [email protected] 2 Physics Faculty, Taras Shevchenko National University of Kyiv, Kyiv 01601, Ukraine; [email protected] 3 Department for Atmospheric Optics and Instrumentation, Main Astronomical Observatory, Kyiv 03143, Ukraine; [email protected] 4 Institute of Physics of the National Academy of Sciences of Belarus, Minsk 220072, Belarus; [email protected] (N.M.); [email protected] (A.B.); [email protected] (A.C.) 5 Astronomical Observatory, Taras Shevchenko National University of Kyiv, Kyiv 04053, Ukraine; [email protected] 6 V. Lashkaryov Institute of Semiconductor Physics of the National Academy of Sciences of Ukraine, Kyiv 03028, Ukraine; [email protected] (A.L.); [email protected] (V.K.); [email protected] (Y.S.) 7 Laboratory of Air Quality, Marzeiev Institute for Public Health, National Academy of Medical Sciences of Ukraine, Kyiv 02660, Ukraine; [email protected] * Correspondence: [email protected] or [email protected]; Tel.: +38-050-3525498

Received: 21 May 2020; Accepted: 30 June 2020; Published: 7 July 2020

Abstract: The spatial and temporal distributions of atmospheric aerosols have been simulated using the GEOS-Chem model over the sparsely investigated Eastern European region. The spatial distribution of the particulate matter (PM2.5) concentration, mineral dust, black carbon, organic aerosols, sea salt, as well as nitrate, sulfate, and ammonium aerosols during 2016–2017 were considered. The aerosols’ concentration, seasonality and spatial features were determined for the region. Particulate matter (PM2.5) contamination prevails in Poland in late autumn and winter. 3 The monthly mean PM2.5 concentration reached 55 µg m− over the Moscow region in the early 3 spring of both years. The mineral dust concentration varied signiﬁcantly, reaching 40 µg m− over the southwestern part of Eastern Europe in March 2016. The areas most polluted by black carbon aerosols were the central and southern parts of Poland in the winter. The organic aerosols’ concentration 3 was the largest in March and April, reaching 10 µg m− over East Belarus. The sea salt aerosol concentration increased in the coastal regions in winter due to the wind strength. Mineral dust aerosols in Eastern Europe are mainly composed of dust, partially transported from the Ukrainian steppe and partially from the Saharan Desert.

Keywords: aerosols; GEOS-Chem; PM2.5; mineral dust; black carbon; organic aerosol; sea salt aerosol

1. Introduction Studies of aerosols’ spatiotemporal distribution on global and regional scales are critical for climate research and air quality monitoring. The aerosol distribution and properties are determined from observational data obtained by various techniques, such as in situ measurements and ground-based and satellite remote sensing, for the purposes of determining the aerosol load and for radiative transfer

Atmosphere 2020, 11, 722; doi:10.3390/atmos11070722 www.mdpi.com/journal/atmosphere Atmosphere 2020, 11, 722 2 of 17 investigations (see, e.g., [1–3]). The remote sensing datasets are rather sparse in time and space, particularly because of the night and clouds. The ground-based sun-photometer measurements of the aerosol optical depth (AOD) are very accurate but limited in time and space [4,5]. The satellite remote sensing measurements of the AOD have a larger coverage area, but the confidence of the satellite data was estimated as low [2]. The spatiotemporal variability of the aerosols’ concentration in the atmosphere varies with altitude because of the short lifetime of the aerosol particles in the troposphere: from hours to weeks. The content and spatiotemporal distribution of the aerosol particles, their chemical composition and microphysical structure, are defined by the physical conditions in the atmosphere and their seasonal variations [6]. The seasonal variations in the aerosol concentration and properties were detected by many observations over the globe in different conditions (e.g., [7–9]). It was estimated from various observations that the mesoscale variability of the aerosol horizontal distribution for spatial scales is 40–400 km and for temporal scales is 2–48 h [10]. The modeling of the spatial and temporal distributions of the aerosol concentration and properties is a technique that allows for the estimation of the complete pattern of the atmospheric aerosols. These models are also an appropriate tool for climatology studies over large domains and long periods of time. To simulate the spatiotemporal distribution of the aerosols’ concentration and properties, different models are used, which are developed by different national and international institutions. For example, the Goddard Earth Observing System-5 (GEOS-5) atmospheric general circulation model (AGCM) is currently in use in the NASA Global Modeling and Assimilation Office (GMAO) at a wide range of resolutions for a variety of applications [11,12]. The widely applicable GEOS-Chem software package is a global three-dimensional (3D) model of atmospheric chemistry controlled by assimilated meteorological observations from the Goddard Earth Observing System (GEOS) of the NASA Data Assimilation Office [13,14]. The widely applicable CHIMERE model [15] is dedicated to the study of regional atmospheric pollution events. This model was applied to study the biomass-burning products’ propagation and their influence on solar radiation in the atmosphere caused by intensive wildfires in the central regions of Russia during the summer of 2010 [16,17] and to study the movement of storm dust from the Sahara and biomass-burning smoke from wildfires (Heinold et al. [18]). Additionally, models are widely used to study the particulate matter (PM2.5) distribution in the atmosphere at regional and global scales (e.g., [19–21]). Modeling of the spatiotemporal distribution of the aerosols’ concentration and their properties is particularly important for regions where up-to-date observations are sparse or absent, such as Eastern Europe. The results of the spatiotemporal distribution of the aerosols and their sources over this region using ground-based and satellite observations are discussed in [9,22,23] and simulated in [24,25]. The AOD at 865 nm were used for the analysis of aerosol properties in the atmosphere over Ukraine and adjacent territories in 2003–2011 by Bovchaliuk et al. [22], with a resolution of approximately 18 km 18 km over cloud-free regions. These POLDER/PARASOL data describe the fine-mode aerosols × from the biomass-burning products and different anthropogenic sources [26]. The increased AOD (865 nm) values have usually been observed in April–May and August–September and agree with AERONET data over the Ukraine and Moldova areas and the South of Russia. An increased AOD (865 nm) to 0.2–0.5 was observed in August 2010 due to the intensive wildfires and fire products by the transboundary transport in July–August 2010 [16,27–29]. The seasonal aerosol properties in the Eastern European region were investigated by Milinevsky et al. [9]. The seasonal variations of the aerosol amount and optical features were analyzed using the AERONET sun-photometers data and POLDER satellite measurements. These areas are influenced by the local aerosol pollution sources (traffic, industry) and by the aerosol transport from remote sources (e.g., open steppe areas, mines, and wildfires). The potential influence of air transport on the seasonal variation of aerosols based on the data from the Kyiv and Minsk AERONET sites has been discussed in [9]. The aerosol properties in the atmosphere over Eastern Europe in 2002–2019 have been investigated via MODIS data by Filonchyk et al. [30]. Atmosphere 2020, 11, 722 3 of 17

The global chemical transport model, GEOS-Chem, was used to simulate the spatiotemporal distributions of atmospheric concentrations of speciﬁc aerosol types over the Belarus–Ukraine region for 2010–2013 [24,25]. Results of the model simulation were evaluated using data from the Minsk and Kyiv AERONET sites. To provide a correct comparison, the simulated aerosol component concentrations were converted into coarse, ﬁne, and total aerosol column volume concentrations, taking into account the hygroscopic growth of particles. The model-predicted and remotely sensed aerosol volume concentrations are in reasonably good agreement. The main purpose of this paper is to determine typical aerosols and reveal the distinctive features of their spatiotemporal distributions in the atmosphere over Belarus and Ukraine where detailed in situ aerosol observations are not available. We use the GEOS-Chem model to simulate the variation of aerosols’ monthly properties in the atmosphere over the Eastern European region, with a special focus on Belarus and Ukraine. In these countries, the air quality networks are not developed and currently are in the stage of planning and discussion. The aerosol distribution in the near-surface layer below 100 m has been modeled. This layer is important to study the impact of aerosols on air quality. In Section2, the GEOS-Chem model is shortly described, and an example of the simulation of the PM2.5 spatial distribution is presented. In Section3, the results of the distribution of various types of aerosols over Eastern Europe according to the GEOS-Chem model are considered, followed by a discussion in Section4. The conclusions are presented in Section5.

2. Materials and Methods GEOS-Chem, a global three-dimensional chemical transport model, is a convenient tool for evaluating the spatial distribution and transport of aerosols and other atmospheric chemical constituents [11,13]. The model was developed and used by research groups worldwide as a versatile tool, applicable to a broad range of atmospheric composition problems. General management of GEOS-Chem is provided by the team based at Harvard University and Dalhousie University and supported by the U.S. NASA Earth Sciences Division, the Canadian National Science and Engineering Research Council, and the Nanjing University of Information Sciences and Technology (http://www.geos-chem.org). The model has been successfully applied to investigate a number of tropospheric chemistry processes (see e.g., [31–34]). The GEOS-Chem Classic configuration uses archived meteorological data from the NASA GMAO on a latitude–longitude grid to model horizontal and vertical transport (http://www.geos-chem.org/). Tracer advection in the GEOS-Chem Classic is performed using a semi-Lagrangian scheme created by Lin and Rood [35]. In this model, the dry deposition is based on the resistance-in-series method [36,37]. The scheme describes dry deposition caused by the turbulent transport in the aerodynamic layer, molecular diffusion through the boundary layer, and uptake at the surface. The aerosol dry deposition also considers the gravitational settling [38–41]. The wet deposition scheme includes the scavenging in convective updrafts, the in-cloud rainout, and the below-cloud washout [42,43]. GEOS-Chem uses the Harvard–NASA Emissions Component (HEMCO) (http://wiki.seas.harvard.edu/geos-chem/ index.php/HEMCO;[44]) to calculate emissions from different sources. The HEMCO can read several types of emission inventories including anthropogenic, biomass-burning, dust, sea salt, biogenic, and others. GEOS-Chem includes the detailed tropospheric chemistry of HOx-NOx-VOC and ozone-halogen-aerosol. The GEOS-Chem model allows for the study of the spatial and temporal distribution of atmospheric species. The results of model processing include the aerosol species: elemental (black) carbon, organic, mineral dust, sea salt, nitrate, sulfate, and ammonium. The aerosol simulations include the emissions and chemistry of nitrate-sulfate-ammonium aerosols coupled to gas-phase chemistry [45], carbonaceous aerosols [46,47], dust [39], and sea salt [40,41]. In this work, we used the 11-01 version of the GEOS-Chem model driven by GEOS-FP meteorology. We used the “tropchem” mechanism (“full chemistry” in the troposphere only) with secondary organic aerosols. Atmosphere 2020, 11, 722 4 of 17

The layout of the Eastern European region considered for the study of aerosol spatiotemporal Atmosphere 2020, 11, x FOR PEER REVIEW 4 of 18 distribution is shown in Figure1a. To avoid overloading the figures, the coordinates and city names willwill bebe omittedomitted fromfrom further further figuresfigures representing representing modeled modeled aerosol aerosol distribution. distribution. TheThe bordersborders ofof thethe countriescountries andand seasseas in in figures figures will will assist assist to to define define the the discussed discussed areas. areas.

FigureFigure 1.1.( a(a)) The The layout layout of of the the Eastern Eastern European European region region covered covered by by modeling; modeling; (b(b)) an an example example of of the the model distribution of the monthly averaged particulate matter (PM2.5) concentrations in the model distribution of the monthly averaged particulate matter (PM2.5) concentrations in the atmosphere overatmosphere the region over in Aprilthe region 2016. in April 2016.

TheThe simulationsimulation waswas performedperformed onon 2 2° ×2.5 2.5°horizontal horizontal resolutionresolution latitudelatitude/longitude/longitude cellscells withwith ◦ × ◦ 4747 verticalvertical layerslayers upup toto 8080 km.km. TheThe cellcell centercenter coordinatescoordinates werewere atat 40,40, 42,42, ...,..., 6060°◦ N; N; 20,20, 22.5,22.5, ...,..., 4040°◦ E E (see(see Figure Figure1 b).1b). In In total, total, the the 99 99 cells cells covered covered the the discussed discussed Eastern Eastern European European region, region, which which includes includes thethe territoriesterritories ofof Estonia,Estonia, Latvia,Latvia, Lithuania,Lithuania, Moldova,Moldova, Romania,Romania, Bulgaria,Bulgaria, Serbia,Serbia, NorthNorth Macedonia,Macedonia, Albania,Albania, asas wellwell asas aa substantialsubstantial partpart ofof Poland,Poland, Slovakia,Slovakia, Hungary,Hungary, Greece,Greece, Montenegro,Montenegro, Turkey, andand Russia.Russia. TheThe AzovAzov SeaSea andand thethe BlackBlack SeaSea werewere coveredcovered entirely,entirely, asas wellwell asas partpart ofof thethe BalticBaltic Sea.Sea. TheThe calculationscalculations havehave beenbeen providedprovided forfor thethe ﬁrstfirst verticalvertical GEOS-ChemGEOS-Chem layer,layer, fromfrom thethe surfacesurface upup toto aboutabout 100100 m.m. TheThe datadata havehave beenbeen averagedaveraged monthlymonthly inin 2016–2017 2016–2017 and and are are presented presented for for each each year. year. The The PM PM2.52.5,, mineralmineral dust,dust, black black carbon, carbon, organic organic aerosol, aerosol, sea sea salt, salt, nitrate, nitrate, sulfate, sulfate, and and ammonium ammonium distributions distributions are discussed.are discussed. For eachFor celleach in cell Figures in Figures1b,2,3,4 ,1b–7,5,6 and the7, theaverage average concentration concentration in inmicrograms micrograms per per cubic 3 cubicmeter meter (µg m (µ−3g) mis −displayed) is displayed through through a color a color scale. scale. For Foreach each aerosol aerosol type, type, the thescale scale was was chosen chosen by byaccounting accounting for for the the highest highest aerosol aerosol value, value, while while the the scale scale was was different diﬀerent for eacheach componentcomponent andand remainedremained the the same same for for one one component component for allfor 24 all months. 24 months. The seasonal The seasonal behavior behavior of the nitrate of the (NO nitrate3−), 2 + sulfate(NO3−), (SO sulfate4 −), and(SO4 ammonium2−), and ammonium (NH4 ) aerosols (NH4+) aerosols is considered, is considered, with an emphasis with an emphasis on the cold on (January), the cold warm(January), (July), warm and transitional(July), and transitional (March, November) (March, November) months. months.

3.3. ResultsResults

3.1. PM2.5 3.1. PM2.5

The particulate matter (PM2.5) is defined as aerosol particles with an aerodynamic diameter The particulate matter (PM2.5) is defined as aerosol particles with an aerodynamic diameter of of less than 2.5 µm. In GEOS-Chem, the PM2.5 is considered as the sum of the nitrate, sulfate, less than 2.5 µm. In GEOS-Chem, the PM2.5 is considered as the sum of the nitrate, sulfate, ammonium, organic aerosol, black carbon, and sea salt in accumulation mode with an effective radius ammonium, organic aerosol, black carbon, and sea salt in accumulation mode with an effective of 0.01–0.50 µm, mineral dust with an effective radius centered at 0.7 µm, and 38% of the mineral radius of 0.01–0.50 µm, mineral dust with an effective radius centered at 0.7 µm, and 38% of the dust with an effective radius centered at 1.4 µm. The PM2.5 calculation accounts for aerosol water mineral dust with an effective radius centered at 1.4 µm. The PM2.5 calculation accounts for aerosol using hygroscopic growth factors depending on the ambient relative humidity. The change in radius water using hygroscopic growth factors depending on the ambient relative humidity. The change in between the dry and wet aerosol is treated as a shell of water. Aerosol growth factors are computed radius between the dry and wet aerosol is treated as a shell of water. Aerosol growth factors are using aerosol densities and aerosol radii. computed using aerosol densities and aerosol radii. The PM2.5 concentration spatial distribution and seasonal variations from winter to autumn are presented in Figure 2. A seasonal enhancement of PM2.5 content in wintertime was modeled from November to March in all areas. The most notable features were three areas with increased PM2.5 concentration over the central and southern parts of Poland, Romania, and the area around Atmosphere 2020, 11, 722 5 of 17

The PM2.5 concentration spatial distribution and seasonal variations from winter to autumn are presented in Figure2. A seasonal enhancement of PM 2.5 content in wintertime was modeled from NovemberAtmosphere 2020 to March, 11, x FOR in PEER all areas. REVIEW The most notable features were three areas with increased PM5 of2.5 18 concentration over the central and southern parts of Poland, Romania, and the area around Moscow. 3 AMoscow. signiﬁcant A di significantﬀerence was difference found between was found the PM between2.5 concentrations the PM2.5 concentrations in March and July: in March 50–54 andµg m July:− 3 vs.50–54 15–18 µgµ gm m−3 −vs.. 15–18 µg m−3.

3 −3 FigureFigure 2. 2.The The spatial spatial distribution distribution of of the the monthly monthly averaged averaged PM PM2.5 2.5concentrations concentrations in inµ gµg m −m by by the the GEOS-ChemGEOS-Chem model model calculation calculation in in (a )(a 2016) 2016 and and (b ()b 2017.) 2017.

InIn the the central central and and southern southern parts parts of of Poland, Poland, the the highest highest PM PM2.5 2.5concentrations concentrations were were found found in in 3 JanuaryJanuary (40–50 (40–50µ gµg m −m).−3). This This PM PM2.52.5enveloped enveloped regionregion was was characterized characterized by by a a large large meridional meridional extent, extent, alsoalso covering covering Slovakia Slovakia and and Hungary Hungary in January in January 2016 and 2016 extending and extending to Belarus, to the Belarus, northern the Ukrainian northern region,Ukrainian and Romaniaregion, and in JanuaryRomania 2017. in January 2017. WeWe noted noted that that there there was was an an exceptionally exceptionally high high contamination contamination in in March March 2016 2016 and and 2017 2017 over over the the areaarea around around Moscow Moscow City City that that exceeded exceeded the the PM PM2.52.5concentration concentration values values in in all all considered considered regions regions 3 andand reached reached 54 54µ gµg m −m−.3. The The cleanest cleanest atmosphere atmosphere over over the the area, area, according according to to the the model, model, was was in in the the 3 summer,summer, when when the the PM PM2.52.5values values were were less less than than 15 15µ gµg m −m,−3 close, close to to the the threshold threshold of of contamination contamination allowedallowed by by Directive Directive 2008 2008/50/EC/50/EC on on ambient ambient air air quality quality and and cleaner cleaner air air for for Europe. Europe. A A comparison comparison of of thethe PM PM2.52.5content content showed showed systematically systematically higher higher concentrations concentrations above above Belarus Belarus compared compared to to Ukraine. Ukraine. 3 3 3 3 InIn January, January, the the PM PM2.5 2.5values values were were 35 35µg µg m −m−and3 and 25 25µg µg m −m−in3 in 2016, 2016, and and 38 38µg µg m −m−and3 and 30 30µg µg m −m−in3 in 2017,2017, in in Belarus Belarus and and Ukraine, Ukraine, respectively. respectively. AA similar similar di differenceﬀerence was was found found in in March March and and April April in in both both years. years. This This can can be be explained explained by by the the largerlarger inﬂuence influence of of a high a high PM PM2.5 aerosol2.5 aerosol contamination contamination inPoland in Poland and and Moscow Moscow on Belarus on Belarus than onthan the on territorythe territory of Ukraine. of Ukraine. In summer, In summer, the PM the2.5 PMconcentration2.5 concentration is similar is similar in Belarus in Belarus andUkraine. and Ukraine.

3.2.3.2. Mineral Mineral Dust Dust MineralMineral dust dust contamination contamination prevailed prevailed in in the the southern southern part part of of the the territories territories considered considered here. here. MineralMineral dust dust mainly mainly appeared appeared in in springtime springtime in in the the southwest southwest part part of of the the area, area, with with the the highest highest concentrationconcentration values values in in March March and and April April 2016. 2016. There were significant differences in the distribution and content of mineral dust between years (Figure 3a vs. Figure 3b). In 2017, the mineral dust concentration was much smaller in value and area than in 2016: 25 µg m−3 vs. 40 µg m−3 were the highest values. Atmosphere 2020, 11, 722 6 of 17

There were signiﬁcant diﬀerences in the distribution and content of mineral dust between years (Figure3a vs. Figure3b). In 2017, the mineral dust concentration was much smaller in value and area 3 3 thanAtmosphere in 2016: 2020 25, 11µ, gx FOR m− PEERvs. 40REVIEWµg m − were the highest values. 6 of 18

FigureFigure 3. 3.The The monthlymonthly averagedaveraged mineralmineral dust conc concentration’sentration’s spatial spatial distributions distributions inin (a ()a 2016) 2016 and and (b) (b2017.) 2017.

3.3.3.3. Black Black Carbon Carbon AccordingAccording to to the the model model calculations, calculations, the the black black carbon carbon (soot) (soot) content content (Figure (Figure4 )4) in in November– November– FebruaryFebruary significantly significantly exceeded exceeded the the content content in in April–September. April–September. The The largest largest values values were were obtained obtained 3 3 overover the the industrial industrial area area in Southernin Southern Poland: Poland: 1.8 µ g1.8 m −µgin m January−3 in January and 1.0 andµg m1.0− µgin July. m−3 Thein July. highest The concentrationhighest concentration of black carbon of black (BC) carbon was seen (BC) over was the seen Moscow over the area—in Moscow March, area—in it was March, even largerit was than even 3 thatlarger in Southern than that Poland in Southern at that time, Poland reaching at 1.6thatµ gtime, m− .reaching The sustained 1.6 µg soot m concentrations−3. The sustained from thesoot surroundingconcentrations areas from were the also modeledsurrounding over Romania,areas were but also the correspondingmodeled over values Romania, did not but exceed the 3 0.9correspondingµg m− . The lowestvalues black did not carbon exceed content 0.9 wasµg m calculated−3. The lowest over the black marine carbon area content in each ofwas the calculated seasons. overOver the marine the territory area in of each Ukraine of the andseasons. Belarus, the black carbon concentration was rather small, 3 except in December and January, when the black carbon concentration increased to about 1.0 µg m− 3 in the western part of Ukraine, and to 0.8 µg m− in the southwestern part of Belarus. The soot concentrations calculated for Ukraine and Belarus were significantly lower than the highest value in the region. Over Belarus, the soot content was systematically higher than it was over Ukraine 3 in 2016. In January 2016, the largest values (over 1 µg m− ) were simulated in the western areas of Belarus. The black carbon distribution was more homogeneous in March, with typical values of about 3 0.5 µg m− . The July values were not particularly different from those calculated for Ukraine, at about 3 0.3 µg m− . However, in 2017, the winter black carbon concentrations in the western parts of both 3 countries were much higher than in 2016, reaching 1.3 µg m− . From Figure4, we see that the black 3 carbon content varied from year to year by 0.5 µg m− in the winter months, and increased during a colder winter (2017, in comparison to 2016). Atmosphere 2020, 11, 722 7 of 17 Atmosphere 2020, 11, x FOR PEER REVIEW 7 of 18

FigureFigure 4. 4.The The blackblack carboncarbon monthlymonthly averaged averaged concentrations concentrations in in ( a(a)) 2016 2016 and and ( b(b)) 2017. 2017.

3.4. OrganicOver the Aerosols territory of Ukraine and Belarus, the black carbon concentration was rather small, −3 exceptThe in distribution December and of the January, monthly when averaged the black concentration carbon concentration of organic aerosols,increased simulated to about 1.0 with µg them in the western part of Ukraine, and to 0.8 µg m−3 in the southwestern part of Belarus. The soot GEOS-ChemAtmosphere 2020 model,, 11, x FOR is shownPEER REVIEW in Figure 5. 8 of 18 concentrations calculated for Ukraine and Belarus were significantly lower than the highest value in the region. Over Belarus, the soot content was systematically higher than it was over Ukraine in 2016. In January 2016, the largest values (over 1 µg m−3) were simulated in the western areas of Belarus. The black carbon distribution was more homogeneous in March, with typical values of about 0.5 µg m−3. The July values were not particularly different from those calculated for Ukraine, at about 0.3 µg m−3. However, in 2017, the winter black carbon concentrations in the western parts of both countries were much higher than in 2016, reaching 1.3 µg m−3. From Figure 4, we see that the black carbon content varied from year to year by 0.5 µg m−3 in the winter months, and increased during a colder winter (2017, in comparison to 2016).

3.4. Organic Aerosols The distribution of the monthly averaged concentration of organic aerosols, simulated with the GEOS-Chem model, is shown in Figure 5.

FigureFigure 5. 5.The The spatial spatial distributions distributions of theof the monthly monthly average averag concentrationse concentrations of organic of organic aerosols aerosols in (a) 2016in (a) and2016 (b and) 2017. (b) 2017.

In winter, a high organic aerosol concentration of 8 µg m−3 was calculated over the southern part of Poland. A high organic aerosol concentration of 11 µg m−3 was calculated over Moscow and regions to the west. High concentrations were found in March–April 2016, and for the same period of 2017. This March–April organic aerosol concentration increase was obtained by modeling for the large area that extends over the western regions of Russia, to the eastern border of Belarus and to the north of the northern Ukraine border. An increased organic aerosol concentration of 7–9 µg m−3 also appeared over Ukraine and Southeast Belarus in October in both years. The seasonal behavior of the organic aerosol concentration in the whole area is complicated because the increased organic aerosol concentrations change their locations during the simulation. An increase in organic aerosol contamination was mainly located over the Moscow region in spring (March–April) and early autumn, with variations from year to year also revealed.

Atmosphere 2020, 11, 722 8 of 17

3 In winter, a high organic aerosol concentration of 8 µg m− was calculated over the southern part 3 of Poland. A high organic aerosol concentration of 11 µg m− was calculated over Moscow and regions to the west. High concentrations were found in March–April 2016, and for the same period of 2017. This March–April organic aerosol concentration increase was obtained by modeling for the large area that extends over the western regions of Russia, to the eastern border of Belarus and to the northern 3 Ukraine border. An increased organic aerosol concentration of 7–9 µg m− also appeared over Ukraine and Southeast Belarus in October in both years. The seasonal behavior of the organic aerosol concentration in the whole area is complicated because the increased organic aerosol concentrations change their locations during the simulation. An increase in organic aerosol contamination was mainly located over the Moscow region in spring (March–April)Atmosphere 2020, and11, x FOR early PEER autumn, REVIEW with variations from year to year also revealed. 9 of 18

3.5.3.5. Sea Sea Salt Salt TheThe sea sea salt salt aerosol aerosol distribution distribution was was obviously obviousl connectedy connected to the to main the sources, main sources, which are which marine are areasmarine (Figure areas6). (Figure According 6). According to the model, to the the model, highest the amounts highest of amounts salt particles of salt were particles located were over located the Balticover Seathe andBaltic the Sea Black and Sea, the whichBlack Sea, conﬁrms which the confirms marine originthe marine of this origin type of aerosol.this type The of aerosol. largest seaThe 3 saltlargest concentration sea salt concentration of 7 µg m− ofwas 7 µg over m− the3 was southern over the part southern of the Balticpart of Sea the in Baltic December, Sea in December, February, andFebruary, March. and The sameMarch. sea The salt same concentration sea salt wasconcentr obtainedation by was a simulation obtained overby a thesimulation northwest over of the the Blacknorthwest Sea in of December the Black and Sea February.in December and February.

FigureFigure 6. 6.The The spatial spatial distributions distributions of of the the monthly monthly averaged averaged concentration concentration of of sea sea salt salt in in (a ()a 2016) 2016 and and (b()b 2017.) 2017.

TheThe spread spread of seaof sea salt salt particles particles across across land areasland canareas be can explained be explained by the large by the area large of the area modeling of the cellmodeling and the cell aerosol and transferthe aerosol from transfer the marine from area, the themarine latter area, of which the latter depends of which on the depends wind strength on the andwind dominant strength direction. and dominant The seasonal direction. sea The salt aerosolseasonal content sea salt and aerosol distribution content can and be distribution explained by can the be dominantexplained factor by the in dominant sea salt aerosol factor formation: in sea salt theaeroso windl formation: speed, which the iswind stronger speed, in which the winter is stronger months in (December–Februarythe winter months (December– (e.g., [48,49February])). (e.g., [48,49])).

3.6. Nitrate, Sulfate, and Ammonium Aerosols The nitrate concentration’s distribution (Figure7a,b) displays a number of common features with the BC content (see Figure4). An increased nitrate concentration was observed over a part 3 3 of Poland and the Moscow region, where the values reached 6 µg m− and 18 µg m− in 2016 and 2017, respectively. As expected, the lowest nitrate concentration was calculated above the sea surface, similar to the BC distribution. The Ukraine territory was characterized by relatively low nitrate pollution, which, in particular, was less than over Belarus. An enhanced amount of nitrate appeared in November–March, with a very low concentration in May–September (see Figure7a,b). The nitrate concentration exhibited clear seasonality; it diﬀered between the cold and warm weather periods 3 3 by more than 10-fold, from 1–2 µg m− to 18 µg m− . In January, there was, almost continuously, a high-concentration band in the zonal direction at latitudes 51–55◦ N, which connects the Poland territory and the Moscow region, including the territory of Belarus. In March and November, in both years, this band became weaker, but the increased values over the Belarus territory remained. The model of the sulfate spatial distribution in 2016 and 2017 is shown in Figure7c,d. The levels 3 3 of sulfate concentrations in the area ranged between 1 µg m− and 6 µg m− , with mean values of 3 4 µg m− . This type of aerosol exhibits a strong seasonal increase in the spring months (March–April), with decreasing values from July to January. High sulfate concentrations were seen mostly over the southern and southwestern territories of the region. The increased amount of sulfate covered a large Atmosphere 2020, 11, x FOR PEER REVIEW 10 of 18 part of Romania, Bulgaria and Turkey (April–May), the eastern part of Ukraine (March–June), and a 3 vastThe area model around of Moscowthe sulfate (March–April), spatial distribution reaching in 42016µg mand− . 2017 A prominent is shown feature in Figure in the7c,d. sulfate The 3 levelsdistribution of sulfate was concentrations the high values in over the thearea west ranged part ofbetween Turkey. 1 Thisµg m local−3 and increase 6 µg m of−3, more withthan mean6 µvaluesg m− ofwas 4 µg seen m during−3. This almosttype of all aerosol months exhibits in 2016–2017. a strong seasonal increase in the spring months (March– 3 April),In with the atmosphere decreasing overvalues the from considered July to area, Januar they. ammonium High sulfate concentration concentrations ranged were from seen0.3 mostlyµg m− 3 overto 7 µtheg m southern− (Figure and7e,f). southwestern The highest ammoniumterritories of content the region. was mostlyThe increased found in amount the cold of period sulfate of coveredthe year a from large November part of Romania, to March, Bulgaria especially and March Turkey 2016 (April–May), and January 2017.the eastern In March part for of both Ukraine years, 3 (March–June),the highest concentration and a vast ofarea ammonium around Moscow was 7 µg (March–April), m− in the Moscow reaching region. 4 µg Similar m−3. toA theprominent seasonal featurevariation in inthe nitrate sulfate distribution, distribution the was ammonium the high concentrationvalues over the is lowwest during part of the Turkey. second This half oflocal the increasespring and of more summer than months 6 µg m (May–September−3 was seen during in almost 2016 and all months May–August in 2016–2017. in 2017).

Figure 7. Cont.

Figure 7. The spatial distributions of the monthly averaged (a,b) nitrate, (c,d) sulfate, and (e,f) ammonium concentrations (a,c,e) in 2016, and (b,d,f) in 2017. Atmosphere 2020, 11, x FOR PEER REVIEW 10 of 18

The model of the sulfate spatial distribution in 2016 and 2017 is shown in Figure 7c,d. The levels of sulfate concentrations in the area ranged between 1 µg m−3 and 6 µg m−3, with mean values of 4 µg m−3. This type of aerosol exhibits a strong seasonal increase in the spring months (March– April), with decreasing values from July to January. High sulfate concentrations were seen mostly over the southern and southwestern territories of the region. The increased amount of sulfate covered a large part of Romania, Bulgaria and Turkey (April–May), the eastern part of Ukraine (March–June), and a vast area around Moscow (March–April), reaching 4 µg m−3. A prominent feature in the sulfate distribution was the high values over the west part of Turkey. This local increase of more than 6 µg m−3 was seen during almost all months in 2016–2017.

Atmosphere 2020, 11, 722 10 of 17

FigureFigure 7. 7. TheThe spatial spatial distributions distributions of of the the monthly monthly averaged averaged (a (a,b,b) )nitrate, nitrate, (c (c,d,d) )sulfate, sulfate, and and (e (e,f,f) ) ammoniumammonium concentrations concentrations ( (aa,c,c,e,e)) in in 2016, 2016, and and ( (bb,,dd,,ff)) in in 2017. 2017.

4. Discussion According to the GEOS-Chem model, the distribution of aerosols of various types in Eastern Europe has been analyzed. The model variations in the particulate matter PM2.5 (which is a combination of the diﬀerent aerosols), mineral dust, black carbon (soot), organic aerosols, and sea salt particles were considered in detail. The behavior of the concentration of the nitrate, sulfate, and ammonium aerosols was also assessed. The seasonal changes in the aerosol constituent concentration, based on the results of a two-year model, were discussed with a focus on the spatial distribution and seasonal variations. Particulate matter PM2.5. The lowest PM2.5 values were modeled over the sea. This was especially 3 noticeable for November and January, when the value over the sea was 10 µg m− —three times less than over the land. At the same time, in January, a small PM2.5 concentration was calculated over the territory of Turkey. The PM2.5 values in March and July were more homogeneous over the sea and 3 above the surrounding land, however, at diﬀerent levels: the PM2.5 was about 20 µg m− in March and 3 about 5 µg m− in July. A high PM2.5 concentration was modeled in January 2016 and December 2016–February 2017 3 (40–50 µg m− ) over the central and southern parts of Poland. This corresponds to measurements of when indoor heating is used, as presented in Sówka et al. [50]. There is a clear seasonal cycle in the largest PM2.5 distributions (Figure2) that can be associated with the heating period when coal is used for heating houses in those areas. Increased PM2.5 values are retained for territories with high anthropogenic loads: the South of Poland and the Moscow area. The PM2.5 content was larger in winter 2017, especially in January–February (e.g., [51]). Atmosphere 2020, 11, 722 11 of 17

3 The highest PM2.5 concentration of 54 µg m− was calculated in March, which is the only month during both 2016 and 2017 when the magnitude of the PM2.5 over the Moscow area was greater than that over Poland. In January and November, the highest PM2.5 value was 1.5 times smaller than in March, and the size of the PM2.5 enhanced concentration area was also smaller, especially in the fall. Mineral dust. Mineral dust aerosols represent soil particles suspended in the atmosphere. The comparison of the PM2.5 and mineral dust by distribution and content variations (see Figures2 and3) reveals a significant difference. According to the modeled data, the mineral dust concentration increases over the southern part of the area, while the PM2.5 mostly impacts the western and northeastern areas. This result is confirmed by [52], where the main source of mineral dust is from the Saharan Desert via the Mediterranean Sea. The mineral dust content increased in springtime in the southwestern part of the area, whereas the PM2.5 mostly increased in the winter months. The significant difference in the mineral dust distribution and content from year to year is explained by the difference in atmospheric circulation and Saharan dust outbreak in spring 2016, when a large amount of mineral dust was transported from North Africa to Europe. The mineral dust enhancement in the eastern part of Ukraine was caused by wind erosion in the steppe region in March and May 2017, similar to the strong dust storm in 2007 [53]. Black carbon. Black carbon is a constituent of particulate matter that results from the incomplete combustion of coal and other fossil fuels, vehicle engine operation, and biomass-burning. The enhanced concentration of inorganic carbon is mostly related to the production of energy at thermal power plants and the heating of houses, especially if coal is used. Although black carbon comprises a small part of atmospheric aerosols over Europe (less than 10% of the PM2.5 if we compare Figures2 and4), it might cause a significant increase in radiative forcing (e.g., [54]). A seasonal cycle with a peak in the cold-weather period, when there is a need for heating the house, is seen over Central and Eastern Europe [50,55,56]. Our model calculations show that the black carbon content in November–February (the heating season) significantly exceeds the content in April–September (the non-heating season). The high values 3 3 were simulated over industrial areas in Southern Poland: 1.8 µg m− in January and 1.0 µg m− in July, which is similar to the elemental carbon data in [50]. The black carbon concentration over the Moscow 3 area was at its largest in March, amounting to about 1.6 µg m− , which is close to the values measured during the AeroRadCity-2018 spring aerosol experiment in Moscow [57]. These comparisons confirm the reliability of the GEOS-Chem evaluations of black carbon content. Secondary organic aerosols. The organic component of aerosol particles is a complex mixture of hundreds of organic compounds. Secondary organic aerosols, formed in the atmosphere by the oxidation of organic gases, represents a major fraction of global submicron-sized atmospheric organic aerosols [58]. According to our simulation, the organic aerosol concentration is particularly high over the polluted urban regions of Eastern Europe, such as the western regions of Russia, the Moscow region, the South of Poland, and the central part of Ukraine, in late autumn, winter and early spring. 3 The largest organic aerosol concentrations were over 11 µg m− , which is comparable with the results of [50], showing organic aerosol (in particular, organic carbon) concentrations over Poland’s cities of 3 3 about 5 µg m− in the non-heating season and 17 µg m− in the heating season. The increase in the organic aerosol concentration also corresponds to the large black carbon and PM2.5 concentrations over the Moscow area in March in both years, according to our data. These features of the organic aerosol distribution suggest that anthropogenic sources are related to fuel-burning products in the regions under study. The simulated organic aerosol distribution also correlates to the PM distribution over the region under study during the cold season, because organic aerosols are involved in PM. The results of our simulation of the organic aerosol spatial distribution and level of concentrations correspond to their common features described above. Our results also suggest a mainly anthropogenic genesis of organic aerosol sources in the regions under study, but their influence on the air quality and climate changes have to be studied more carefully, using both model simulation and remote and in situ observations. Atmosphere 2020, 11, 722 12 of 17

Sea salt. Sea salt aerosols are introduced into the atmosphere as primary particles of natural origin, mainly by sea spray. Sea salt particles represent about 30% of the global column aerosol optical depth. In many areas, the sea salt aerosol concentration is close to that of water vapor, corresponding to the wet scavenging dominant role of sea salt aerosol removal from the atmosphere. However, wet scavenging in a drier and colder winter atmosphere is less than in a wetter summer atmosphere ([49] and citations therein), so proper quantification of the sea salt aerosol content is an important task for a better implementation of sea salt aerosols in climate models [48]. We have supposed that the sea salt aerosol has a predominantly (and perhaps almost exclusively) marine origin. There are two distinct areas with the highest sea salt concentration in the studied territory: the Black Sea and the Baltic Sea. A systematic increase was also noticeable for cells that included a part of the Mediterranean Sea or its branches. However, in January–March and in October 3 and December, a sea salt aerosol concentration of about 1 µg m− was found in most regions under study. This can be considered a result of the sea salt aerosols’ wind transport from both seas. Nitrate, sulfate, and ammonium. Nitrate-sulfate-ammonium aerosols are present in the atmosphere as solid particles, or droplets of water solutions including sulfuric and nitric acid, partially or totally neutralized by ammonia. Sulfate and nitrate aerosols may be present in the atmosphere, and are associated with sodium, or calcium, or other metals. A small fraction of the sulfate aerosol is emitted as sea salt primary particles. Most nitrate-sulfate-ammonium aerosols are formed in the atmosphere through the oxidation and neutralization of precursor gases: sulfur dioxide (SO2), nitric oxides (NOx), and ammonia (NH3)[45]. The major anthropogenic source of SO2 and NO2 is fossil fuel combustion. Important natural sources of NO2 include lightning, soils, and wildfire. Major sources of ammonia emissions are animals, fertilizers, industry, fossil fuels, biomass-burning, oceans, crops, and soils [59]. As precursor gases generally have a lifetime of about one day, the distribution of nitrate-sulfate-ammonium aerosols corresponds to the distribution of sources of sulfur dioxide, nitric oxides, and ammonium [45,59]. According to our calculations, the highest nitrate concentration values are in the range of 3 6–18 µg m− , depending on the year. The modeled ammonium concentration varies within the range of 3 0.3–7 µg m− over Eastern Europe, with increased values in the autumn, winter, and spring in the eastern part of the area and in the Moscow region (Figure7e,f). The nitrate and ammonium spatial distributions and seasonal behaviors are similar in general (Figure7a,b,e,f). The model concentration of these aerosol species exhibits clear seasonal variations, with increasing values in November–March. The GEOS-Chem model simulations of the nitrate-sulfate-ammonium aerosols, as well as comparisons to observations in Europe, have been provided by Park et al. [45]. The comparison of model calculations and the European Monitoring and Evaluation Programme observations indicate the determination coefficient R2 = 0.48, 0.63, and 0.72 for nitrate, ammonium, and sulfate, respectively [45]. Ammonia in the atmosphere originates predominantly from the agricultural industry, fossil fuels, fires, and biomass-burning [60]. Ambient ammonia concentrations change over several orders of magnitude, which complicates comparisons between measurements and models. The comparison of the GEOS-Chem model data with both remote and in situ measurements was performed in [60,61]. Schiferl et al. [61] concluded that the GEOS-Chem simulation underestimates the ammonia concentration near the main sources by 26%. Zhu et al. [62] implemented into GEOS-Chem the bidirectional exchange of ammonium, + considering the equilibrium between ammonia (NH3) and ammonium (NH4 ). They emphasized that the GEOS-Chem model typically underestimates ammonium concentrations, sometimes by 2- or 5-fold, in the spring and autumn. The monthly variations in the sulfate concentration (Figure7c,d) show the highest seasonal values in spring and early summer, unlike nitrate and ammonium. The sulfate spatial distribution also demonstrates differences from those of nitrate and ammonium, with the highest sulfate concentration over the southern and southwestern areas of Eastern Europe, similar to the distribution modeled by Yang et al. [63]. The area of increased sulfate concentration is seen repeatedly every month (Figure7c,d). Atmosphere 2020, 11, 722 13 of 17

This feature is located in the western part of Turkey and requires more detailed consideration in future work. The analysis of the seasonal variations and distribution of aerosol concentration shows some similarities between the different species. There are some specific features described above, but in general the highest aerosol concentration is simulated over the Poland and Moscow region. In most cases, the model demonstrates, in particular, higher concentrations in Belarus than in Ukraine. The reliability of such a conclusion requires verification based on direct observations by a sun photometer, LiDAR, and in situ measurements. For most aerosol species—PM2.5, black carbon, and organic aerosols— a clear seasonal cycle exists, with the largest values found in the colder period of the year.

5. Conclusions The content of this paper is a simulation by the GEOS-Chem model and a consideration of the monthly averaged properties of the aerosols’ distribution over Eastern Europe in the near-surface layer below 100 m for 2016–2017. The GEOS-Chem v11-01 version model, driven by GEOS-FP meteorology with the “tropchem” mechanism, was used. The areas under consideration also include the Azov Sea, the Black Sea, and the southern part of the Baltic Sea. The PM2.5, black carbon, mineral dust, organic aerosol, sea salt, nitrate, sulfate, and ammonium distributions were discussed. Comparisons of the GEOS-Chem model data with observations at various altitudes in the atmosphere have been undertaken by other authors [45,59,62,63]. These comparisons show a reasonable agreement between the model and the observed data, which in general justiﬁes the results of this paper. An increased PM2.5 concentration corresponds to urban areas, so this type of aerosol is likely of mostly anthropogenic origin. The mineral dust aerosols are not distributed like PM2.5, and are composed of dust hypothetically transported by the wind from the Sahara Desert and the Ukrainian steppe. The content of black carbon in the atmosphere is mostly related to the production of energy by thermal power plants and the heating of houses, which use coal. Our simulations show that the black carbon content in November–February signiﬁcantly exceeded that of April–September, essentially over urban territories, similar to the PM2.5 distribution. According to our simulation, the organic aerosol concentration is substantially higher over polluted urban regions of Eastern Europe and corresponds to a high concentration of black carbon and PM2.5. These results suggest a mainly anthropogenic origin of organic aerosols in Eastern Europe. Sea salt aerosols are introduced into the atmosphere as primary particles of natural origin by sea spray. The sea salt aerosols’ concentration reaches the highest values over the Black Sea and the Baltic Sea. An almost negligible concentration of sea salt aerosols over the land under consideration can be considered a result of the wind transport of sea salt aerosols from both seas. GEOS-Chem modeling provided a general view of concentration levels and seasonal aerosol variations, which are on average consistent with the measurements and models in other regions (see e.g., [62,63]). The results of the GEOS-Chem aerosol properties simulation in our region allowed us to understand the needs and extent of development of the comprehensive air-quality network in countries that are sparsely covered by aerosol measurements. Therefore, GEOS-Chem modeling makes it possible to create a baseline of the studied aerosol spatial distributions and to determine the areas and species that have to be investigated in more detail.

Author Contributions: Conceptualization, G.M. and N.M.; Formal analysis, A.G. and V.D.; Funding acquisition, A.L., V.K., and Y.S.; Investigation, Y.Y. and Y.W.; Methodology, N.M. and A.G.; Project administration, G.M. and N.M.; Software, N.M.; Supervision, G.M.; Validation, G.M. and V.D.; Writing—original draft, G.M., N.M., and V.D.; Writing—review and editing, G.M., N.M., V.D., A.B., A.C., Y.Y., Y.W., A.L., V.K., and O.T. All authors wrote parts of the text, contributed to the discussion of the results, helped edit the manuscript. All authors have read and agreed to the published version of the manuscript. Funding: This research received no external funding. Acknowledgments: This work was supported in part by the College of Physics, International Center of Future Science, Jilin University, China, and by Taras Shevchenko National University of Kyiv, projects 19BF051-08 and 20BF051-02. The authors would also like to acknowledge the NATO project No. G5500 and the Atmosphere 2020, 11, 722 14 of 17

SMURBS/ERA-PLANET/GEO Essential project supported by the European Commission “Horizon 2020” program. The authors thank the two anonymous referees for their useful comments and suggestions. Conﬂicts of Interest: The authors declare no conﬂict of interest.

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